Sensitivity of Adzuki Bean (<i>Vigna angularis</i>) to Acifluorfen, Fomesafen, Bentazon, Imazethapyr and Halosulfuron-Methyl Applied Postemergence
Bibliographic record
Abstract
New herbicide options are needed for postemergence (POST) broadleaf weed control in adzuki bean. A field study, of five experiments, was conducted over a three-year period (2014, 2015, 2016) in Ontario to evaluate the tolerance of adzuki bean to the POST application of acifluorfen (600 and 1200 g·ai·ha-1), fomesafen (240 and 480 g·ai·ha-1), bentazon (1080 and 2160 g·ai·ha-1), imazethapyr (100 and 200 g·ai·ha-1) and halosulfuron-methyl (75 and 150 g·ai·ha-1). Acifluorfen and fomesafen applied POST caused as much as 12% visible injury at the 1X rate and 20% visible injury at the 2X rate but had no adverse effect on adzuki bean population, shoot dry weight, height, maturity or yield. Bentazon caused as much as 23% visible injury at 1080 g·ai·ha-1 and 28% visible injury at 2160 g·ai·ha-1 but caused no adverse effect on adzuki bean population, shoot dry weight, height, maturity or yield at either rate, except at 2160 g·ai·ha-1 which reduced shoot dry weight 20% and height 12%. Imazethapyr caused as much as 22% visible injury at 100 g·ai·ha-1 and 34% visible injury at 200 g·ai·ha-1 but caused no adverse effect on adzuki bean population, shoot dry weight, height, maturity or yield at either rate except at 200 g·ai·ha-1 which delayed maturity slightly. Halosulfuron-methyl caused as much as 65% visible injury and reduced shoot dry weight, height and yield 64%, 41%, and 28%, respectively. This research concludes that acfluorfen, fomesafen, bentazon, imazethapyr and halosulfuron at the rates evaluated can cause the significant injury in adzuki bean.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".